import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf

				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				enum.Enum			):


		'''simple docstring'''






		lowerCAmelCase_     =   0
		lowerCAmelCase_     =   1





@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'generated'
		def __init__(       self     :     int     ,  *snake_case__     :     List[str]     ,  **snake_case__     :     str		)      ->	Tuple:
				super().__init__(*snake_case__     ,  **snake_case__		)

				self.check_model_type(
				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				    if self.framework == 'tf'
				    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING		)
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     str=None     ,  snake_case__     :     Optional[Any]=None     ,  snake_case__     :     Tuple=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     Optional[int]=None     ,  **snake_case__     :     Optional[Any]     ,  )      ->	List[Any]:
				_lowerCamelCase				   =  {}
				if truncation is not None:
						_lowerCamelCase				   =  truncation

				_lowerCamelCase				   =  generate_kwargs

				_lowerCamelCase				   =  {}
				if return_tensors is not None and return_type is None:
						_lowerCamelCase				   =  ReturnType.TENSORS if return_tensors else ReturnType.TEXT
				if return_type is not None:
						_lowerCamelCase				   =  return_type

				if clean_up_tokenization_spaces is not None:
						_lowerCamelCase				   =  clean_up_tokenization_spaces

				if stop_sequence is not None:
						_lowerCamelCase				   =  self.tokenizer.encode(snake_case__     ,  add_special_tokens=snake_case__		)
						if len(snake_case__		) > 1:
								warnings.warn(
								    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
								    ' the stop sequence will be used as the stop sequence string in the interim.'		)
						_lowerCamelCase				   =  stop_sequence_ids[0]

				return preprocess_params, forward_params, postprocess_params
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Any:
				return True
		def   _snake_case       (       self     :     List[Any]     ,  *snake_case__     :     Tuple     ,  snake_case__     :     Dict		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.model.config.prefix if self.model.config.prefix is not None else ''
				if isinstance(args[0]     ,  snake_case__		):
						if self.tokenizer.pad_token_id is None:
								raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'		)
						_lowerCamelCase				   =  ([prefix + arg for arg in args[0]],)
						_lowerCamelCase				   =  True

				elif isinstance(args[0]     ,  snake_case__		):
						_lowerCamelCase				   =  (prefix + args[0],)
						_lowerCamelCase				   =  False
				else:
						raise ValueError(
						    f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""		)
				_lowerCamelCase				   =  self.tokenizer(*snake_case__     ,  padding=snake_case__     ,  truncation=snake_case__     ,  return_tensors=self.framework		)
				# This is produced by tokenizers but is an invalid generate kwargs
				if "token_type_ids" in inputs:
						del inputs["token_type_ids"]
				return inputs
		def __call__(       self     :     str     ,  *snake_case__     :     List[Any]     ,  **snake_case__     :     Optional[int]		)      ->	Optional[Any]:
				_lowerCamelCase				   =  super().__call__(*snake_case__     ,  **snake_case__		)
				if (
				    isinstance(args[0]     ,  snake_case__		)
				    and all(isinstance(snake_case__     ,  snake_case__		) for el in args[0]		)
				    and all(len(snake_case__		) == 1 for res in result		)
				):
						return [res[0] for res in result]
				return result
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     List[str]     ,  snake_case__     :     int=TruncationStrategy.DO_NOT_TRUNCATE     ,  **snake_case__     :     Tuple		)      ->	Optional[Any]:
				_lowerCamelCase				   =  self._parse_and_tokenize(snake_case__     ,  truncation=snake_case__     ,  **snake_case__		)
				return inputs
		def   _snake_case       (       self     :     Any     ,  snake_case__     :     int     ,  **snake_case__     :     Union[str, Any]		)      ->	int:
				if self.framework == "pt":
						_lowerCamelCase   ,							_lowerCamelCase				   =  model_inputs['input_ids'].shape
				elif self.framework == "tf":
						_lowerCamelCase   ,							_lowerCamelCase				   =  tf.shape(model_inputs['input_ids']		).numpy()

				_lowerCamelCase				   =  generate_kwargs.get('min_length'     ,  self.model.config.min_length		)
				_lowerCamelCase				   =  generate_kwargs.get('max_length'     ,  self.model.config.max_length		)
				self.check_inputs(snake_case__     ,  generate_kwargs['min_length']     ,  generate_kwargs['max_length']		)
				_lowerCamelCase				   =  self.model.generate(**snake_case__     ,  **snake_case__		)
				_lowerCamelCase				   =  output_ids.shape[0]
				if self.framework == "pt":
						_lowerCamelCase				   =  output_ids.reshape(snake_case__     ,  out_b // in_b     ,  *output_ids.shape[1:]		)
				elif self.framework == "tf":
						_lowerCamelCase				   =  tf.reshape(snake_case__     ,  (in_b, out_b // in_b, *output_ids.shape[1:])		)
				return {"output_ids": output_ids}



		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     List[Any]     ,  snake_case__     :     Union[str, Any]=ReturnType.TEXT     ,  snake_case__     :     str=False		)      ->	Tuple:
				_lowerCamelCase				   =  []
				for output_ids in model_outputs["output_ids"][0]:
						if return_type == ReturnType.TENSORS:
								_lowerCamelCase				   =  {f"""{self.return_name}_token_ids""": output_ids}
						elif return_type == ReturnType.TEXT:
								_lowerCamelCase				   =  {
								    f"""{self.return_name}_text""": self.tokenizer.decode(
								        snake_case__     ,  skip_special_tokens=snake_case__     ,  clean_up_tokenization_spaces=snake_case__     ,  )
								}
						records.append(snake_case__		)
				return records





@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'summary'
		def __call__(       self     :     Tuple     ,  *snake_case__     :     Optional[Any]     ,  **snake_case__     :     Optional[Any]		)      ->	List[str]:
				return super().__call__(*snake_case__     ,  **snake_case__		)



		def   _snake_case       (       self     :     str     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	bool:
				if max_length < min_length:
						logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}."""		)

				if input_length < max_length:
						logger.warning(
						    f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
						    'a summarization task, where outputs shorter than the input are typically wanted, you might '
						    f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""		)





@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   'translation'
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Optional[int]:
				if input_length > 0.9 * max_length:
						logger.warning(
						    f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
						    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'		)
				return True
		def   _snake_case       (       self     :     List[str]     ,  *snake_case__     :     Any     ,  snake_case__     :     Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE     ,  snake_case__     :     List[Any]=None     ,  snake_case__     :     Any=None		)      ->	int:
				if getattr(self.tokenizer     ,  '_build_translation_inputs'     ,  snake_case__		):
						return self.tokenizer._build_translation_inputs(
						    *snake_case__     ,  return_tensors=self.framework     ,  truncation=snake_case__     ,  src_lang=snake_case__     ,  tgt_lang=snake_case__		)
				else:
						return super()._parse_and_tokenize(*snake_case__     ,  truncation=snake_case__		)
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     str=None     ,  snake_case__     :     List[Any]=None     ,  **snake_case__     :     Optional[Any]		)      ->	List[Any]:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  super()._sanitize_parameters(**snake_case__		)
				if src_lang is not None:
						_lowerCamelCase				   =  src_lang
				if tgt_lang is not None:
						_lowerCamelCase				   =  tgt_lang
				if src_lang is None and tgt_lang is None:
						# Backward compatibility, direct arguments use is preferred.
						_lowerCamelCase				   =  kwargs.get('task'     ,  self.task		)
						_lowerCamelCase				   =  task.split('_'		)
						if task and len(snake_case__		) == 4:
								# translation, XX, to YY
								_lowerCamelCase				   =  items[1]
								_lowerCamelCase				   =  items[3]
				return preprocess_params, forward_params, postprocess_params



		def __call__(       self     :     Dict     ,  *snake_case__     :     Union[str, Any]     ,  **snake_case__     :     str		)      ->	List[str]:
				return super().__call__(*snake_case__     ,  **snake_case__		)
import functools
from typing import Any


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				list[str]							)   ->    bool:

		# Validation
		if not isinstance(UpperCamelCase			,       UpperCamelCase							) or len(UpperCamelCase							) == 0:
				raise ValueError('the string should be not empty string'							)

		if not isinstance(UpperCamelCase			,       UpperCamelCase							) or not all(
		    isinstance(UpperCamelCase			,       UpperCamelCase							) and len(UpperCamelCase							) > 0 for item in words							):
				raise ValueError('the words should be a list of non-empty strings'							)

		# Build trie
		_lowerCamelCase				   =  {}
		_lowerCamelCase				   =  'WORD_KEEPER'

		for word in words:
				_lowerCamelCase				   =  trie
				for c in word:
						if c not in trie_node:
								_lowerCamelCase				   =  {}

						_lowerCamelCase				   =  trie_node[c]

				_lowerCamelCase				   =  True

		_lowerCamelCase				   =  len(UpperCamelCase							)

		# Dynamic programming method
		@functools.cache
		def is_breakable(UpperCamelCase     :				int							) -> bool:
				if index == len_string:
						return True

				_lowerCamelCase				   =  trie
				for i in range(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  trie_node.get(string[i]			,       UpperCamelCase							)

						if trie_node is None:
								return False

						if trie_node.get(UpperCamelCase			,       UpperCamelCase							) and is_breakable(i + 1							):
								return True

				return False

		return is_breakable(0							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()